Joint Information Theoretic and Differential Geometrical Approach for Robust Automated Target Recognition

Abstract

The overall objective of this project is to develop transformative theory and algorithms for robust Automated Target Recognition (ATR). This project addressed the following challenging problems in ATR: modeling uncertainty, small sample size, high dimensional data, irrelevant features/dimensions, heterogeneous data, and outliers. In this project, the PI proposed and developed the following new techniques: 1) kernel local feature extraction (KLFE) for ATR applications, 2) technique for identifying network dynamics under sparsity and stationarity constraints, 3) self-organized-queue-based (SOQ) clustering scheme, 4) robust principal component analysis (RPCA) based on manifold optimization, outlier detection, and subspace decomposition.

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Document Details

Document Type
Technical Report
Publication Date
Feb 29, 2012
Accession Number
ADA564135

Entities

People

  • Dapeng Wu

Organizations

  • University of Florida

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automated Target Recognition
  • Classification
  • Computational Science
  • Dimensionality Reduction
  • Factor Analysis
  • Feature Extraction
  • Identification
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Mathematical Models
  • Network Science
  • Recognition
  • Target Recognition
  • Three Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms